def _cal_pe_ratio_1_by_manual(order_book_id):
ret = []
raw_day_reports = _day_metrics(order_book_id)
raw_quarter_reports = _quarter_metrics(order_book_id)
enddate_report_map = _enddate_reports_map(raw_quarter_reports)
day_df = DataFrame(raw_day_reports).fillna(numpy.nan)
market_caps = day_df.market_cap.values
tradedates = day_df.tradedate.values
for i in range(len(tradedates)):
t = int(to_datetime(tradedates[i]).strftime('%Y%m%d'))
ret_value = {'tradedate': t}
latest_enddates = _latest_enddates(t)
report = None
for latest_enddate in latest_enddates:
latest_report = enddate_report_map.get(latest_enddate)
if latest_report is None:
continue
ann_date = latest_report.get('announce_date')
if t >= ann_date:
report = latest_report
ret_value['announce_date'] = ann_date
ret_value['end_date'] = latest_enddate
break
if report is not None:
nppc_value = report.get('net_profit_parent_company')
if nppc_value is not None and nppc_value != 0:
market_cap = market_caps[i]
ret_value['pe_ratio_1'] = round(market_cap / nppc_value, 4)
ret_value['net_profit_parent_company'] = nppc_value
ret.append(ret_value)
return ret
def _cal_ev_by_manual(order_book_id):
ret = []
raw_day_reports = _day_metrics(order_book_id)
raw_quarter_reports = _quarter_metrics(order_book_id)
enddate_report_map = _enddate_reports_map(raw_quarter_reports)
day_df = DataFrame(raw_day_reports).fillna(numpy.nan)
tradedates = day_df.tradedate.values
val_stk_rights = day_df.val_of_stk_right.values
for i in range(len(tradedates)):
t = int(to_datetime(tradedates[i]).strftime('%Y%m%d'))
ret_value = {'tradedate': t}
latest_enddates = _latest_enddates(t)
report = None
for latest_enddate in latest_enddates:
latest_report = enddate_report_map.get(latest_enddate)
if latest_report is None:
continue
ann_date = latest_report.get('announce_date')
if t >= ann_date:
report = latest_report
ret_value['announce_date'] = ann_date
ret_value['end_date'] = latest_enddate
break
ret_value['ev'] = 0
val_stk_value = val_stk_rights[i]
if val_stk_value is not None:
ret_value['ev'] += val_stk_value
if report is not None:
debt = report.get('interest_bearing_debt')
if debt is not None:
ret_value['ev'] += debt
ret_value['interest_bearing_debt'] = debt
ret.append(ret_value)
return ret
def test_pcf_ratio_3(self):
stockcode = '000002.XSHE'
quarter_metrics = _get_quarter_metrics(stockcode)
year_quarters_map = strategy_quarter_index_on_year(quarter_metrics)
stockcode_without_suffix = stockcode.split(".")[0]
market_caps = _day_metrics(stockcode_without_suffix)
four_straight_quarter_cash_flow = _four_straight_quarter(
quarter_metrics, 'cash')
four_straight_quarter_increment_cash_flow = _four_straight_increment_quarter(
four_straight_quarter_cash_flow, 'cash')
pcf_ratio_3_dict = {
'TRD_DATE': [],
'pcf_ratio_3_recal': [],
'TCAP_1': []
}
print(four_straight_quarter_cash_flow)
print(four_straight_quarter_increment_cash_flow)
for index, record in enumerate(market_caps):
recal_four_straight_indicator(
record, four_straight_quarter_increment_cash_flow,
year_quarters_map, 'pcf_ratio_3', ['cash', 'cash_equivalent'])
pcf_ratio_3_dict['TRD_DATE'].append(record['tradedate'])
pcf_ratio_3_dict['pcf_ratio_3_recal'].append(record['pcf_ratio_3'])
pcf_ratio_3_dict['TCAP_1'].append(record['market_cap'])
calc_df = DataFrame(pcf_ratio_3_dict)
calc_df.to_csv(
'~/Desktop/eod_indicator_identify/000002_pcf_ratio_3_identify.csv')
def test_pb_ratio(self):
stockcode = '000001.XSHE'
quarter_metrics = _get_quarter_metrics(stockcode)
year_quarters_map = strategy_quarter_index_on_year(quarter_metrics)
stockcode_without_suffix = stockcode.split(".")[0]
market_caps = _day_metrics(stockcode_without_suffix)
pb_ratio_dict = {'TRD_DATE': [], 'pb_ratio_recal': [], 'pb_ratio_manual': [],
'TCAP_1': [], 'isdifference': []}
manual_df = read_csv('~/Desktop/recal_manual/000001_pb_ratio.csv')
for index, record in enumerate(market_caps):
recal_latest_quarter_indicator(record, quarter_metrics,
year_quarters_map, 'pb_ratio', ['current_assets'])
pb_ratio_manual = float(manual_df['pb_ratio'][index])
if math.isnan(pb_ratio_manual):
pb_ratio_manual = None
else:
pb_ratio_manual = Decimal(manual_df['pb_ratio'][index]).quantize(Decimal('0.0000'))
if record['pb_ratio'] == pb_ratio_manual:
record['isdifference'] = ""
else:
record['isdifference'] = "******"
pb_ratio_dict['TRD_DATE'].append(record['tradedate'])
pb_ratio_dict['pb_ratio_recal'].append(record['pb_ratio'])
pb_ratio_dict['pb_ratio_manual'].append(pb_ratio_manual)
pb_ratio_dict['TCAP_1'].append(record['market_cap'])
pb_ratio_dict['isdifference'].append(record['isdifference'])
calc_df = DataFrame(pb_ratio_dict)
calc_df.to_csv('~/Desktop/eod_indicator_identify/000001_pb_ratio_identify.csv')
self.assertEqual(manual_df['pb_ratio'].equals(calc_df['pb_ratio_recal']), True)
def _cal_four_latest_by_manual(order_book_id, metric_name, recal_metric_name):
ret = []
raw_day_reports = _day_metrics(order_book_id)
raw_quarter_reports = _quarter_metrics(order_book_id)
four_latest_reports = _four_latest_quarter(raw_quarter_reports, metric_name)
enddate_four_latest_map = _enddate_reports_map(four_latest_reports)
day_df = DataFrame(raw_day_reports).fillna(numpy.nan)
market_caps = day_df.market_cap.values
tradedates = day_df.tradedate.values
for i in range(len(tradedates)):
t = int(to_datetime(tradedates[i]).strftime('%Y%m%d'))
ret_value = {'tradedate': t}
latest_enddates = _latest_enddates(t)
report = None
for latest_enddate in latest_enddates:
latest_report = enddate_four_latest_map.get(latest_enddate)
if latest_report is None:
continue
ann_date = latest_report.get('announce_date')
if t >= ann_date:
report = latest_report
ret_value['announce_date'] = ann_date
ret_value['end_date'] = latest_enddate
break
if report is None:
ret.append(ret_value)
continue
metric_value = report.get(metric_name)
if metric_value is None or metric_value == 0:
ret.append(ret_value)
continue
ret_value[metric_name] = metric_value
ret_value[recal_metric_name] = round(market_caps[i] / metric_value, 4)
ret.append(ret_value)
return ret
def _get_day_metrics(order_book_id):
return _day_metrics(order_book_id)